SoroushVahidi/scidocs huggingface dataset:
Determining embedding dimension for phase-space reconstruction using a geometrical construction.
(1992)

A, et al. Nonlinear analysis of electroencephalogram at rest and during cognitive tasks in patients with schizophrenia. One-dimensional electroencephalography (EEG) data were transformed into multidimensional phase space. The phase space concept is crucial for nonlinear dynamics analysis. In a hypothetical system governed by n variables, the phase space is n-dimensional. Each state of the system corresponds to a point in phase space, with n coordinates that are the values assumed by the governing variables for this specific state. If the system is observed for a period of time, the sequence of the point in phase space converges into a subspace, called system's attractor. The technique of delay coordinates is carried out to reconstruct the attractor's trajectory. To unfold the projection back to a multivariate state space, a representation of the original system, the delay coordinates y(t) = [xj(t), xj(t + T), … xj(t + [d-1] T)] from a single time series xj and the embedding procedure were performed. In this formula, y(t) is a point of the trajectory in the phase space at time t, x (t is iT) are the coordinates in the phase space corresponding to the time-delayed values of the time series, T is the time delay between the points of the time series considered, and d is the embedding dimension. 1,2 The choice of the optimal time delay T and embedding dimension d are important to the success of reconstruction with finite data. For the time delay, the first local minimum of the average mutual information between the set of measurements v(t) and v(t + T) was used in the present study. For the embedding dimension d the minimum (optimal) embedding was computed in the reconstruction procedure. 3 The attractor's reconstruction is necessary for the analysis of mathematical quantities characterizing the attractor itself. One important metric property of the attractor is the correlation dimension (D2), which estimates the degrees of freedom, such as the number of independent variables necessary to describe the dynamic of the original system. The larger the D2 values of the attractor, the more complicated the behavior of the nonlinear system. The D2 is thus a measure of the complexity of the process being investigated, and it characterizes the distribution of points in phase space. 4 The Gassberger–Procaccia algorithm 5 was computed to evaluate D2 values of the attractors from EEG data. With this algorithm, D2 is derived by determining the relative number …
Obstacle Avoidance System for a Quadrotor UAV
(2012)

This is the summary of the research being done at Cal Poly Pomona at an undergraduate level on the development of obstacle avoidance capability for unmanned aerial vehicles (UAVs). A quadrotor UAV is used as the research platform. Push and Rapidly Exploring Random Tree (RRT) algorithms are used for the obstacle avoidance capability. The paper shows the use of cheaper commercial off-the-shelf sensors and processor board in the design and implementation of obstacle avoidance algorithm.
A new linearization method of unbalanced electrical distribution networks
(2014)

With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. As DN control and operation strategies are mostly based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages, line currents, power loss) and control variables (e.g., power injections, transformer tap positions), efficient and precise calculation of these sensitivity coefficients, i.e. linearization of DN, is of fundamental importance. In this paper, the derivation of the node voltages and power loss as functions of the nodal power injections and transformers' tap-changers positions is presented, and then solved by a Gauss-Seidel method. Compared to other approaches presented in the literature, the proposed method takes into account different load characteristics (e.g., constant PQ, constant impedance, constant current and any combination of above) of a generic multi-phase unbalanced DN and improves the accuracy of linearization. Numerical simulations on both IEEE 13 and 34 nodes test feeders show the efficiency and accuracy of the proposed method.
Dielectric properties estimation of normal and malignant skin tissues at millimeter-wave frequencies using effective medium theory
(2014)

Millimeter-wave reflectometry can be used as a non-invasive method for early detection of skin cancers. Less penetration and higher resolution are only some reasons to use mm-waves for skin cancer diagnosis. The dielectric parameters data of human skin at mm-wave frequencies are not widely addressed in the literature due to the expensive instruments and the complicated structure of human skin. Available dielectric properties of cancerous tissues in the literature are for other tissues rather than skin and are restricted to the frequencies below 20 GHz. In this paper, dielectric properties of healthy and malignant skin tissues were estimated in the range of 20-100 GHz by applying the effective medium theory. A stratified skin model and some skin tumors were presented. The possibility of mm-wave reflectometry for detection of skin cancers has also been studied. The results of this paper can be used as a theoretical basis for taking further steps in skin cancer research.
Ontology-Based Data Access for Extracting Event Logs from Legacy Data: The onprom Tool and Methodology
(2017)

Process mining aims at discovering, monitoring, and improving business processes by extracting knowledge from event logs. In this respect, process mining can be applied only if there are proper event logs that are compatible with accepted standards, such as extensible event stream (XES). Unfortunately, in many real world set-ups, such event logs are not explicitly given, but instead are implicitly represented in legacy information systems. In this work, we exploit a framework and associated methodology for the extraction of XES event logs from relational data sources that we have recently introduced. Our approach is based on describing logs by means of suitable annotations of a conceptual model of the available data, and builds on the ontology-based data access (OBDA) paradigm for the actual log extraction. Making use of a realworld case study in the services domain, we compare our novel approach with a more traditional extract-transform-load based one, and are able to illustrate its added value. We also present a set of tools that we have developed and that support the OBDA-based log extraction framework. The tools are integrated as plugins of the ProM process mining suite.
Identifying the Higgs Boson
(2015)

This paper discusses the identification of the Higgs boson subatomic particle from jet pull energy colorflow images of the particles’ decay, as modeled by the ATLAS Experiment at the Large Hadron Collider in CERN [1]. The Higgs field is an hypothesized energy field thought to permeate the entire universe. Without it, the Standard Model of particle physics would break down, as atomic particles would not have the required mass to attract each other, leading them to simply float around in the universe at the speed of light [20]. Its proof would completely alter our understanding of mass as a physical property, making the discovery of the Higgs field the fundamental unanswered question in particle physics in the last halfcentury [13]. The Standard Model suggests that if the Higgs field were to exist, then its quantum excitation, a particle referred to as the Higgs boson, would also have to exist [20]. The Large Hadron Collider (LHC), tasked with finding this particle, consisting of multiple super-powered electromagnets, collides charged particles traveling at near lightspeed. These collisions deform space upon impact, breaking the charged particles into subatomic constituents. It has been hypothesized that with a proton collision at high enough energy, the Higgs boson would decay in observable ways. The ATLAS detector at the LHC records 40 million proton collisions a second, making human curation of these events unfeasible [14]. An accurate classification system that would label the most promising observations as Higgs boson particle decays is, therefore, required. In collaboration with SLAC and the ATLAS Experiment at CERN we were given access to energy images for both Higgs boson and gluon decay, referred to as signal and background respectively, seen in Figures 1 and 2. The purpose of this project was to build a binary classifier which, given the colorflow energy image of the decay of an unknown particle, would accurately distinguish whether or not that particle was a Higgs boson. While we tested a wide array of supervised learning models, we focused primarily on ensemble methods. In particular we developed and fine-tuned an Adaptive Boosting classifier (AdaBoost) with a Random Forest classifier as its base estimator [8, 10]. We also utilized a number of image feature extraction mechanisms includ-
A new cell search scheme in 3GPP long term evolution downlink, OFDMA systems
(2009)

This paper presents a cell search procedure in 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) downlink systems. The proposed cell search procedure contains three steps. In the beginning, conventional mode detection, symbol timing detection and fractional carrier frequency offset (CFO) estimation methods are adopted so that frequency-domain signal processing can be performed subsequently. Secondly, a new joint detection method for integer carrier frequency offset and sector cell index information is proposed, which is shown to be capable of resisting symbol timing error. Finally, we also proposed a detection method for frame timing and cell identity (ID) group information without explicit channel estimates. Simulation results demonstrate that the proposed scheme with lower detection error probability is effective and robust compared with some conventional approaches.
Approximate graph mining with label costs
(2013)

Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to define a cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that approximate mining yields interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.
Evaluating features and classifiers for road weather condition analysis
(2016)

Weather-dependent road conditions are a major factor in many automobile incidents; computer vision algorithms for automatic classification of road conditions can thus be of great benefit. This paper presents a system for classification of road conditions using still-frames taken from an uncalibrated dashboard camera. The problem is challenging due to variability in camera placement, road layout, weather and illumination conditions. The system uses a prior distribution of road pixel locations learned from training data then fuses normalized luminance and texture features probabilistically to categorize the segmented road surface. We attain an accuracy of 80% for binary classification (bare vs. snow/ice-covered) and 68% for 3 classes (dry vs. wet vs. snow/ice-covered) on a challenging dataset, suggesting that a useful system may be viable.
Multi-modal and multi-brain-computer interfaces: A review
(2015)

In this short paper we survey recent research views in non-traditional brain-computer interfaces. That is, interfaces that can process brain activity input, but that are designed for non-clinical purposes, that are meant to be used by `healthy' users, that process other user input as well, and that even allow input, voluntarily or involuntarily, from multiple users. This is a rather new research and BCI application area. Control of applications can be made more robust by fusing brain activity information with other information, either explicitly provided by a user (such as commands) or extracted from the user by interpreting his or her behavior (movements, posture, gaze, facial expression, nonverbal speech) or sensing (neuro-)physiological characteristics (skin conductivity, heart rate, brain activity). We see also research where brain activity of multiple users is fused to get a (sometimes critical) control task done. We see also the emergence of applications where BCI measurements help to improve group performance, for example, during meetings, or where information obtained from these measurements helps to learn about audience preferences. Multi-brain BCI has also become a paradigm in artistic and game applications. Artistic interactive BCI applications may require audience participation. In game environments brain activity of various players can be used to control or adapt the game. Both competition and collaboration in serious games, entertainment games and artistic installations requires fusion of EEG measurements from different subjects.
FACTORS AFFECTING CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE IN EGYPT
(2011)

The study makes a significant contribution to the corporate social responsibility (CSR) disclosure literature by offering the first study of its type undertaken in Egypt as an example of a developing country that examines the determinants of individual and aggregated types of CSR information. Using a sample of 111 Egyptian listed companies for the period of 2005–2010, we find that 66% of the Egyptian listed companies disclose on average 10–50 CSR statements. In addition, we find that product/customer information is used extensively by Egyptian listed companies compared with other types of CSR information. Finally we find that profitability is the main determinant for the aggregated and most of individual CSR information in Egypt.
Automatic fault tree generation from SysML system models
(2014)

In this paper, a methodology is proposed to integrate safety analysis within a systems engineering approach. This methodology is based on SysML models and aims at generating (semi-) automatically safety analysis artifacts, mainly FMEA and FTA, from system models. Preliminary functional and component FMEA are automatically generated from the functional and structural models respectively, then completed by safety experts. By representing SysML structural diagram as a directed multi-graph, through a graph traversal algorithm and some identified patterns, generic fault trees are automatically derived with corresponding logic gates and events. The proposed methodology provides the safety expert with assistance during safety analysis. It helps reducing time and error proneness of the safety analysis process. It also helps ensuring consistency since the safety analysis artifacts are automatically generated from the latest system model version. The methodology is applied to a real case study, the electromechanical actuator EMA.
Securing communication in 6LoWPAN with compressed IPsec
(2011)

Real-world deployments of wireless sensor networks (WSNs) require secure communication. It is important that a receiver is able to verify that sensor data was generated by trusted nodes. It may also be necessary to encrypt sensor data in transit. Recently, WSNs and traditional IP networks are more tightly integrated using IPv6 and 6LoWPAN. Available IPv6 protocol stacks can use IPsec to secure data exchange. Thus, it is desirable to extend 6LoWPAN such that IPsec communication with IPv6 nodes is possible. It is beneficial to use IPsec because the existing end-points on the Internet do not need to be modified to communicate securely with the WSN. Moreover, using IPsec, true end-to-end security is implemented and the need for a trustworthy gateway is removed. In this paper we provide End-to-End (E2E) secure communication between IP enabled sensor networks and the traditional Internet. This is the first compressed lightweight design, implementation, and evaluation of 6LoWPAN extension for IPsec. Our extension supports both IPsec's Authentication Header (AH) and Encapsulation Security Payload (ESP). Thus, communication endpoints are able to authenticate, encrypt and check the integrity of messages using standardized and established IPv6 mechanisms.
Structural equations modeling : Fit Indices , sample size , and advanced topics
(2010)

This article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth. © 2009 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.
A series elastic actuator as a new load-sensitive continuously variable transmission mechanism for control actuation systems
(2013)

Electrical motors are often used to actuate mechanisms with varying speed and under varying load e.g., missile control actuation systems or robot extremities. Sizing of the motor is critical in these applications where space is limited. Varying speed and load cause poor utilization of the motor if the reduction ratio is constant. This requires employment of a larger motor than the smallest possible with ideal variable transmission. To realize better utilization of the motor, we propose a series elastic actuator that works as a load sensitive continuously variable transmission system. This is achieved by combining a pin in slot mechanism with an elastic output shaft. The proposed mechanism is modeled and simulated using example missile flight data and a typical robot finger loading scenario. Results are compared to the constant reduction ratio case, and advantages of the proposed mechanism are shown as decreased actuator size and power.
From Manual to Semi-Automatic Semantic Annotation: About Ontology-Based Text Annotation Tools
(2000)

Semantic Annotation is a basic technology for intelligent content and is bene cial in a wide range of content-oriented intelligent applications, esp. in the area of the Semantic Web. In this paper we present our work in ontology-based semantic annotation, which is embedded in a scenario of a knowledge portal application. Starting with seemingly good and bad manual semantic annotation, we describe our experiences made within the KAinitiative. The experiences gave us the starting point for developing an ergonomic and knowledge base-supported annotation tool. Furthermore, the annotation tool described are currently extended with mechanisms for semi-automatic information-extraction based annotation. Supporting the evolving nature of semantic content we additionally describe our idea of evolving ontologies supporting semantic annotation. This paper has been presented at the COLING-2000 Workshop on Semantic Annotation and Intelligent Content, Centre Universitaire, Luxembourg, 5.-6. August, 2000.
Towards Text Knowledge Engineering
(1998)

We introduce a methodology for automating the maintenance of domain-specific taxonomies based on natural language text understanding. A given ontology is incrementally updated as new concepts are acquired from real-world texts. The acquisition process is centered around the linguistic and conceptual “quality” of various forms of evidence underlying the generation and refinement of concept hypotheses. On the basis of the quality of evidence, concept hypotheses are ranked according to credibility and the most credible ones are selected for assimilation into the domain knowledge base.
Introduction to WordNet: An On-line Lexical Database
(1990)

Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought.
Exploring Students' Computational Thinking Skills in Modeling and Simulation Projects: a Pilot Study
(2015)

Computational Thinking (CT) is gaining a lot of attention in education. We explored how to discern the occurrences of CT in the projects of 12th grade high school students in the computer science (CS) course. Within the projects, they constructed models and ran simulations of phenomena from other (STEM) disciplines. We examined which CT aspects occurred in students' activities and how to assess students' CT accomplishments. For this purpose we employed a framework based on CT characterizations by Wing [14, 15], CSTA [4] and Comer et al. [3]. We analyzed students' project documentation, survey results and interviews with individual students. The findings indicate that this framework is suitable for detection of occurrences of CT aspects in students' data. Moreover, our preliminary results suggest that the framework is useful in assessment of the quality of the students' CT performance.
Influence and passivity in social media
(2011)

The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa.
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
(2017)

A major challenge in brain tumor treatment planning and quantit ative evaluation is determination of the tumor extent. The noninva s ve magnetic resonance imaging (MRI) technique has emerged as a front-line diag nostic tool for brain tumors without ionizing radiation. Manual segmentation of brain t umor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, w e propose a fully automatic method for brain tumor segmentation, which is develo ped using U-Net based deep convolutional networks. Our method was evalu ated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cro svalidation has shown that our method can obtain promising segmentation effi-